i'm trying to get the Pearson correlation coefficient for all rows in a data frame relative to each other. there are values that are empty (NA) and this seems to be presenting a problem that I don't encounter when running cor() on 2 vectors with missing values. This is the correct result on 2 vectors:
x <- c(NA, 4.5, NA, 4, NA, 1)
y <- c(2.5, 3.5, 3, 3.5, 3, 2.5)
cor(x,y, use = "complete.obs")
[1] 0.9912407
and here is the result when they are part of a data frame:
cor(t(critics1), use = "complete.obs")
y a b c d e x
y 1 NA NA NA NA NA NA
a NA 1 1 1 -1 1 -1
b NA 1 1 1 -1 1 -1
c NA 1 1 1 -1 1 -1
d NA -1 -1 -1 1 -1 1
e NA 1 1 1 -1 1 -1
x NA -1 -1 -1 1 -1 1
Warning message:
In cor(t(critics1), use = "complete.obs") : the standard deviation is zero
Why is the use parameter not having the same effect? Here is what the critics1 dataframe looks like;
film1 film2 film3 film4 film5 film6
y 2.5 3.5 3.0 3.5 3.0 2.5
a 3.0 3.5 1.5 5.0 3.0 3.5
b 2.5 3.0 NA 3.5 4.0 NA
c NA 3.5 3.0 4.0 4.5 2.5
d 3.0 4.0 2.0 3.0 3.0 2.0
e 3.0 4.0 NA 5.0 3.0 3.5
x NA 4.5 NA 4.0 NA 1.0
As @joran speculated, when you transpose critics1, there are only two complete observations (i.e. rows with no missing values). That's why all of the correlations are either 1 or -1 or (for those involving y, which has value 3.5 in both complete rows), NA.
t(critics1)
# y a b c d e x
# film1 2.5 3.0 2.5 NA 3 3.0 NA
# film2 3.5 3.5 3.0 3.5 4 4.0 4.5
# film3 3.0 1.5 NA 3.0 2 NA NA
# film4 3.5 5.0 3.5 4.0 3 5.0 4.0
# film5 3.0 3.0 4.0 4.5 3 3.0 NA
# film6 2.5 3.5 NA 2.5 2 3.5 1.0
If you use use="pairwise.complete.obs" instead of use="complete.obs", it works as you'd like:
cor(t(df), use="pairwise.complete.obs")["y","x"] # Extract correlation of y and x
# [1] 0.9912407